Overview

Dataset statistics

Number of variables10
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory390.8 KiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation
cut has 158 (3.2%) zerosZeros
color has 608 (12.2%) zerosZeros
clarity has 64 (1.3%) zerosZeros

Reproduction

Analysis started2024-04-12 15:40:32.407717
Analysis finished2024-04-12 15:40:40.468312
Duration8.06 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

carat
Real number (ℝ)

HIGH CORRELATION 

Distinct202
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.794486
Minimum0.23
Maximum4.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:40.527581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.3
Q10.4
median0.7
Q31.04
95-th percentile1.68
Maximum4.13
Range3.9
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.46842385
Coefficient of variation (CV)0.58959358
Kurtosis1.1529171
Mean0.794486
Median Absolute Deviation (MAD)0.32
Skewness1.0760275
Sum3972.43
Variance0.2194209
MonotonicityNot monotonic
2024-04-12T12:40:40.646515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 250
 
5.0%
1.01 228
 
4.6%
0.31 208
 
4.2%
0.32 192
 
3.8%
0.7 167
 
3.3%
0.9 144
 
2.9%
1 143
 
2.9%
0.41 125
 
2.5%
0.4 121
 
2.4%
0.5 117
 
2.3%
Other values (192) 3305
66.1%
ValueCountFrequency (%)
0.23 37
 
0.7%
0.24 12
 
0.2%
0.25 17
 
0.3%
0.26 30
 
0.6%
0.27 24
 
0.5%
0.28 14
 
0.3%
0.29 12
 
0.2%
0.3 250
5.0%
0.31 208
4.2%
0.32 192
3.8%
ValueCountFrequency (%)
4.13 1
< 0.1%
3.01 2
< 0.1%
2.53 1
< 0.1%
2.52 2
< 0.1%
2.51 1
< 0.1%
2.5 2
< 0.1%
2.48 1
< 0.1%
2.47 1
< 0.1%
2.44 1
< 0.1%
2.42 2
< 0.1%

cut
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7614
Minimum0
Maximum4
Zeros158
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:40.736525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0353185
Coefficient of variation (CV)0.37492522
Kurtosis-0.037128016
Mean2.7614
Median Absolute Deviation (MAD)1
Skewness-0.68950647
Sum13807
Variance1.0718844
MonotonicityNot monotonic
2024-04-12T12:40:40.817528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3 1992
39.8%
4 1294
25.9%
2 1099
22.0%
1 457
 
9.1%
0 158
 
3.2%
ValueCountFrequency (%)
0 158
 
3.2%
1 457
 
9.1%
2 1099
22.0%
3 1992
39.8%
4 1294
25.9%
ValueCountFrequency (%)
4 1294
25.9%
3 1992
39.8%
2 1099
22.0%
1 457
 
9.1%
0 158
 
3.2%

color
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6088
Minimum0
Maximum6
Zeros608
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:40.896117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6912524
Coefficient of variation (CV)0.64828748
Kurtosis-0.86813777
Mean2.6088
Median Absolute Deviation (MAD)1
Skewness0.16970956
Sum13044
Variance2.8603346
MonotonicityNot monotonic
2024-04-12T12:40:40.999786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 1097
21.9%
1 918
18.4%
2 844
16.9%
4 767
15.3%
0 608
12.2%
5 517
10.3%
6 249
 
5.0%
ValueCountFrequency (%)
0 608
12.2%
1 918
18.4%
2 844
16.9%
3 1097
21.9%
4 767
15.3%
5 517
10.3%
6 249
 
5.0%
ValueCountFrequency (%)
6 249
 
5.0%
5 517
10.3%
4 767
15.3%
3 1097
21.9%
2 844
16.9%
1 918
18.4%
0 608
12.2%

clarity
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0582
Minimum0
Maximum7
Zeros64
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:41.074189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6313629
Coefficient of variation (CV)0.53343893
Kurtosis-0.41124022
Mean3.0582
Median Absolute Deviation (MAD)1
Skewness0.52772921
Sum15291
Variance2.661345
MonotonicityNot monotonic
2024-04-12T12:40:41.153411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 1194
23.9%
3 1136
22.7%
1 845
16.9%
4 801
16.0%
5 464
 
9.3%
6 346
 
6.9%
7 150
 
3.0%
0 64
 
1.3%
ValueCountFrequency (%)
0 64
 
1.3%
1 845
16.9%
2 1194
23.9%
3 1136
22.7%
4 801
16.0%
5 464
 
9.3%
6 346
 
6.9%
7 150
 
3.0%
ValueCountFrequency (%)
7 150
 
3.0%
6 346
 
6.9%
5 464
 
9.3%
4 801
16.0%
3 1136
22.7%
2 1194
23.9%
1 845
16.9%
0 64
 
1.3%

depth
Real number (ℝ)

Distinct121
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.71166
Minimum44
Maximum70.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:41.263813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile59.2
Q161
median61.8
Q362.5
95-th percentile63.8
Maximum70.2
Range26.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4462062
Coefficient of variation (CV)0.023434894
Kurtosis7.0815807
Mean61.71166
Median Absolute Deviation (MAD)0.7
Skewness-0.44876851
Sum308558.3
Variance2.0915123
MonotonicityNot monotonic
2024-04-12T12:40:41.375073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 226
 
4.5%
61.6 203
 
4.1%
62.1 193
 
3.9%
62.2 191
 
3.8%
61.9 186
 
3.7%
62.3 182
 
3.6%
61.8 178
 
3.6%
61.5 177
 
3.5%
61.7 170
 
3.4%
61.3 147
 
2.9%
Other values (111) 3147
62.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
53 1
 
< 0.1%
55.3 1
 
< 0.1%
55.8 2
< 0.1%
55.9 1
 
< 0.1%
56.3 3
0.1%
56.5 2
< 0.1%
56.6 1
 
< 0.1%
56.7 3
0.1%
56.8 3
0.1%
ValueCountFrequency (%)
70.2 1
< 0.1%
69.8 2
< 0.1%
69.6 1
< 0.1%
68.6 1
< 0.1%
68.5 1
< 0.1%
68.1 1
< 0.1%
67.9 1
< 0.1%
67.8 1
< 0.1%
67.6 2
< 0.1%
67.5 1
< 0.1%

table
Real number (ℝ)

Distinct78
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.44706
Minimum51.6
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:41.480554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum51.6
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range43.4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2589989
Coefficient of variation (CV)0.039323142
Kurtosis15.509318
Mean57.44706
Median Absolute Deviation (MAD)1
Skewness1.4767543
Sum287235.3
Variance5.103076
MonotonicityNot monotonic
2024-04-12T12:40:41.587308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 921
18.4%
57 889
17.8%
58 766
15.3%
59 643
12.9%
55 585
11.7%
60 362
 
7.2%
54 236
 
4.7%
61 210
 
4.2%
62 121
 
2.4%
63 59
 
1.2%
Other values (68) 208
 
4.2%
ValueCountFrequency (%)
51.6 1
 
< 0.1%
52 6
 
0.1%
53 50
1.0%
53.1 1
 
< 0.1%
53.3 1
 
< 0.1%
53.4 1
 
< 0.1%
53.5 2
 
< 0.1%
53.6 1
 
< 0.1%
53.7 2
 
< 0.1%
53.8 5
 
0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
70 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 7
 
0.1%
65.4 1
 
< 0.1%
65 8
 
0.2%
64.2 1
 
< 0.1%
64 26
0.5%
63 59
1.2%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3179
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3925.5394
Minimum-1
Maximum18787
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)0.2%
Memory size39.2 KiB
2024-04-12T12:40:41.716498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile539.95
Q1936
median2392.5
Q35369.25
95-th percentile12932.65
Maximum18787
Range18788
Interquartile range (IQR)4433.25

Descriptive statistics

Standard deviation3975.4521
Coefficient of variation (CV)1.0127149
Kurtosis2.0864922
Mean3925.5394
Median Absolute Deviation (MAD)1685.5
Skewness1.589369
Sum19627697
Variance15804220
MonotonicityNot monotonic
2024-04-12T12:40:41.822183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 15
 
0.3%
776 15
 
0.3%
765 15
 
0.3%
561 14
 
0.3%
698 14
 
0.3%
625 13
 
0.3%
526 13
 
0.3%
544 13
 
0.3%
552 12
 
0.2%
878 11
 
0.2%
Other values (3169) 4865
97.3%
ValueCountFrequency (%)
-1 10
0.2%
351 2
 
< 0.1%
357 2
 
< 0.1%
361 1
 
< 0.1%
362 1
 
< 0.1%
363 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
377 1
 
< 0.1%
383 1
 
< 0.1%
ValueCountFrequency (%)
18787 1
< 0.1%
18777 1
< 0.1%
18741 1
< 0.1%
18705 1
< 0.1%
18656 1
< 0.1%
18541 1
< 0.1%
18515 1
< 0.1%
18493 1
< 0.1%
18445 1
< 0.1%
18430 1
< 0.1%

x
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.725188
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:41.941696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.7
median5.69
Q36.54
95-th percentile7.62
Maximum10
Range10
Interquartile range (IQR)1.84

Descriptive statistics

Standard deviation1.1191564
Coefficient of variation (CV)0.19547942
Kurtosis-0.63408023
Mean5.725188
Median Absolute Deviation (MAD)0.92
Skewness0.35052099
Sum28625.94
Variance1.2525111
MonotonicityNot monotonic
2024-04-12T12:40:42.217935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.38 50
 
1.0%
4.37 50
 
1.0%
4.34 46
 
0.9%
4.35 45
 
0.9%
4.32 44
 
0.9%
4.31 40
 
0.8%
4.4 37
 
0.7%
4.29 35
 
0.7%
6.43 34
 
0.7%
4.39 34
 
0.7%
Other values (464) 4585
91.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
3.86 2
 
< 0.1%
3.88 2
 
< 0.1%
3.89 1
 
< 0.1%
3.9 2
 
< 0.1%
3.91 2
 
< 0.1%
3.92 4
0.1%
3.93 4
0.1%
3.94 6
0.1%
3.95 3
0.1%
ValueCountFrequency (%)
10 1
< 0.1%
9.44 1
< 0.1%
9.24 1
< 0.1%
8.89 1
< 0.1%
8.87 1
< 0.1%
8.83 1
< 0.1%
8.8 1
< 0.1%
8.72 1
< 0.1%
8.68 1
< 0.1%
8.64 1
< 0.1%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct471
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.727744
Minimum0
Maximum9.85
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:42.346296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.71
median5.7
Q36.54
95-th percentile7.5805
Maximum9.85
Range9.85
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.1121061
Coefficient of variation (CV)0.19416129
Kurtosis-0.64940276
Mean5.727744
Median Absolute Deviation (MAD)0.92
Skewness0.34133506
Sum28638.72
Variance1.2367801
MonotonicityNot monotonic
2024-04-12T12:40:42.457592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.34 52
 
1.0%
4.39 42
 
0.8%
4.37 39
 
0.8%
4.35 39
 
0.8%
4.41 39
 
0.8%
4.32 39
 
0.8%
4.38 37
 
0.7%
4.33 36
 
0.7%
4.36 35
 
0.7%
4.4 35
 
0.7%
Other values (461) 4607
92.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
3.84 1
 
< 0.1%
3.89 2
 
< 0.1%
3.9 2
 
< 0.1%
3.92 1
 
< 0.1%
3.93 1
 
< 0.1%
3.94 2
 
< 0.1%
3.95 6
0.1%
3.96 5
0.1%
3.97 4
0.1%
ValueCountFrequency (%)
9.85 1
< 0.1%
9.37 1
< 0.1%
9.13 1
< 0.1%
8.93 1
< 0.1%
8.87 1
< 0.1%
8.83 1
< 0.1%
8.78 1
< 0.1%
8.66 1
< 0.1%
8.65 1
< 0.1%
8.62 1
< 0.1%

z
Real number (ℝ)

HIGH CORRELATION 

Distinct304
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.533076
Minimum0
Maximum6.43
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-04-12T12:40:42.559313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.65
Q12.9
median3.53
Q34.03
95-th percentile4.68
Maximum6.43
Range6.43
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.69033367
Coefficient of variation (CV)0.19539168
Kurtosis-0.59720822
Mean3.533076
Median Absolute Deviation (MAD)0.57
Skewness0.34845832
Sum17665.38
Variance0.47656057
MonotonicityNot monotonic
2024-04-12T12:40:42.662485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 77
 
1.5%
2.73 72
 
1.4%
2.72 72
 
1.4%
2.7 66
 
1.3%
2.71 66
 
1.3%
2.68 65
 
1.3%
2.67 59
 
1.2%
2.74 58
 
1.2%
3.99 56
 
1.1%
4.02 56
 
1.1%
Other values (294) 4353
87.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.41 1
 
< 0.1%
2.35 1
 
< 0.1%
2.37 1
 
< 0.1%
2.38 1
 
< 0.1%
2.39 2
 
< 0.1%
2.4 2
 
< 0.1%
2.41 1
 
< 0.1%
2.42 4
0.1%
2.43 6
0.1%
ValueCountFrequency (%)
6.43 1
< 0.1%
6.16 1
< 0.1%
5.73 1
< 0.1%
5.62 1
< 0.1%
5.6 1
< 0.1%
5.53 1
< 0.1%
5.43 1
< 0.1%
5.42 1
< 0.1%
5.41 1
< 0.1%
5.39 1
< 0.1%

Interactions

2024-04-12T12:40:39.392009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.548423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.463260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.214957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.928554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.659009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.531247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.224545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.967798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.680303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.639649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.662071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.542675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.290036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.003712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.733113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.605169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.302837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.041050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.752813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.715056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.758355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.621676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.368020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.082524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.809194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.678984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.384394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.115329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.835429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.784918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.832284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.696926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.437521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.153004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.880233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.746558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.459060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.190368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.906811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.857320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.909722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.775094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.512742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.230771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.953794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.822843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.537333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.273086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.981979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.925024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:32.982400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.846406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.581071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.302078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.021496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.888965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.609426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.341309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.050459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.990292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.051403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.916606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.647919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.370489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.085247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.951627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.677573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.406804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.117146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:40.064250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.133264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.994452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.722316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.446188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.160768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.022922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.752964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.481295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.190462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:40.131098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.208153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.066401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.791238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.515650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.388729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.090331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.822458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.545322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.258100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:40.200296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:33.388350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.138272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:34.857932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:35.587578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:36.460007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.155284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:37.894526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:38.609668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-12T12:40:39.323044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-04-12T12:40:42.735813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
caratclaritycolorcutdepthpricetablexyz
carat1.000-0.3460.2430.020-0.0020.9580.2050.9950.9950.993
clarity-0.3461.0000.0420.042-0.071-0.179-0.148-0.341-0.336-0.347
color0.2430.0421.0000.0320.0270.1410.0350.2380.2390.243
cut0.0200.0420.0321.000-0.2890.0390.0730.0400.023-0.007
depth-0.002-0.0710.027-0.2891.000-0.020-0.264-0.057-0.0580.073
price0.958-0.1790.1410.039-0.0201.0000.1820.9570.9570.952
table0.205-0.1480.0350.073-0.2640.1821.0000.2130.2050.169
x0.995-0.3410.2380.040-0.0570.9570.2131.0000.9980.988
y0.995-0.3360.2390.023-0.0580.9570.2050.9981.0000.987
z0.993-0.3470.243-0.0070.0730.9520.1690.9880.9871.000

Missing values

2024-04-12T12:40:40.297525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T12:40:40.413831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caratcutcolorclaritydepthtablepricexyz
01.1034162.055.047336.616.654.11
11.2934262.656.064246.966.934.35
21.2045261.158.055106.886.804.18
31.5032260.956.087707.437.364.50
40.9022361.757.044936.176.213.82
50.3233761.754.09184.394.422.72
60.3024762.958.07894.264.292.69
70.6131561.354.028235.515.593.40
82.0143161.257.2187058.088.144.97
90.3135461.255.05074.374.392.68
caratcutcolorclaritydepthtablepricexyz
49900.7332661.956.051545.755.793.57
49910.4233362.254.08474.804.822.99
49920.7235561.057.026025.815.843.55
49931.0021159.659.043216.486.513.87
49940.7133461.555.034315.725.773.54
49950.3834562.353.38324.654.692.91
49960.3343561.359.09274.454.422.72
49971.2536562.156.059806.816.844.24
49980.3142362.958.08024.314.272.70
49990.3034661.257.06554.304.392.66

Duplicate rows

Most frequently occurring

caratcutcolorclaritydepthtablepricexyz# duplicates
00.3141361.659.08724.354.322.672